Speech recognition technology has been increasingly deployed for a variety of purposes, including electronic dictation, voice command recognition, and telephone-based customer service engines. Speech recognition typically involves the processing of acoustic signals that are received via a microphone. In doing so, a speech recognition engine is typically utilized to interpret the acoustic signals into words or grammar elements. In certain environments, such as vehicular environments, the use of speech recognition technology enhances safety because drivers are able to provide instructions in a hands-free manner.
Conventional in-vehicle speech interfaces typically utilize a hierarchy of grammar elements to control various vehicle functions. For example, in order to tune a radio, a user might state “radio,” listen for confirmation, state “channel,” listen for confirmation, and then state “101.9.” The conventional hierarchical approach is typically cumbersome and time consuming for a user. Several attempts have been made to flatten out speech hierarchies in order to permit more direct commands. These attempts include the use of larger vocabulary sizes and the addition of natural language processing. However, only marginal improvements have been obtained. Indeed, the use of relatively small vocabularies with rich phonemic signatures appears to provide more accurate speech recognition results under varying acoustical conditions associated with a vehicle. Accordingly, there is an opportunity for improved systems and methods for targeting speech recognition to specific functions associated with a vehicle.